Now we comment our result by comparing it with the existing ones in the literature. In [1]}, they used the coal mining disasters data recorded per day and proposed a reversible jump MCMC approach to detect changepoints and estimate the intensity function. According to the Figure 2 in the same paper, the model with two changepoints has the highest posterior probability. Moreover, according to their Figure 3, in the two changepoints scenario, the posterior mode is approximately 14,000 days for the first changepoint and 35,000 days for the second one. This is very close to our result since counting from March 15th, 1851, 14,000 days is between the year 1889 and 1890 and 35,000 days is the time between the year 1946 and 1947. Later, a Bayesian binary segmentation procedure was proposed by [2]} to locate changepoints for Poisson process. Based on two different tests they adopted, their procedure obtained two different sets of changepoints (one changepoint for applying Bayes factor criterion and two for applying BIC approximation criterion) where the locations of changepoints for these two models are quite similar to the results (1) and (2) mentioned in the last paragraph. On the other hand, as it was pointed out in [3]}, UK parliament passed several acts to improve the safety of mine works including the Coal Mines Regulation Acts of 1872 and 1887 and a further one in 1954 with mines and quarries acts. In general, it is reasonable to have a non-increasing expectation of the number of disasters after the year releasing these regulations. As it is shown in Figure REF , the model with two changepoints meets the releasing regulation years 1887 and 1954. Considering the best fit with the time of released regulations and the results given in the literatures, we believe the two changepoints model for this dataset is the most reasonable one to the truth.
